The present invention relates to techniques for computing residual resource consumption for top-k data reports, and in particular, relates to resampling of resource consumption reports so as to include long term resource consumption.
Monitoring systems are important to performance management of computer systems because they may collect a wide range of different types of metrics representing the state of the computer system. Typically, such monitoring systems keep newer metrics at a high sampling rate, whereas older metrics are resampled to a lower sampling rate. For standard metric types, such as quantities and counters, resampling the data is straight forward. However, to observe computing clusters, other metric types, such as the top-k processes consuming a given resource (such as CPU time, memory, etc.) may be preferable.
Top-k metrics report the top-k entities consuming a particular resource over a given time window (for example, a time window of 10 seconds). If such reports are to be resampled, multiple reports may be taken together to form a new top-k report covering a larger time window (for example, 30 seconds). Problems may occur in that the 3 10 second reports may not be representative for resources consumed over the 30 second window.
Accordingly, a need arises for a technique by which computer system metrics may be resampled that provides improved accuracy over conventional techniques.